Post-3.14 week two: uv 0.9.7, Ruff 0.14.3, pip 25.3, Polars 1.35.1, and a couple of ML/data nudges

The week after the “upgrade everything” rush stayed productive: packaging and linting both shipped fresh tags, Polars cut a release, and there’s still healthy momentum on agent tooling. If you’re running Python 3.14 in anger, this is a good window to bump, test, and lock.

We briefly updated the Snake Signals website to a Halloween theme last week and there will be more themes to come! Check it out on my LinkedIn if you didn’t see already - link at the bottom of this post to my page.

uv keeps humming. Astral published uv 0.9.7 on Oct 30 with fresh wheels across platforms. If you’re evaluating uv for CI speed, pin 0.9.7 and re-time your “green build” before and after so you can decide with numbers.

Ruff rolled again. 0.14.3 landed Oct 30. Update your pre-commit hook so the whole team formats and lints against the same rules, then gate any new checks as warnings for one sprint.

pip 25.3 is last week’s drop but still relevant as teams catch up. Treat it as a normal monthly upgrade and do a quick smoke test on editable installs and custom backends. It’s the final scheduled pip release of 2025.

Data stack: Polars 1.35.1 shipped Oct 30. If your analytics or ETL path is testing 3.14, this is a safe, modern DataFrame option to pin alongside NumPy 2.3.4.

Agents: momentum continued around Pydantic-AI and its examples; multiple example packs released Oct 28–31. If you’re prototyping internal assistants, pin versions so your demos don’t drift.

Macro note: Python 3.14 adoption continues to climb; most of the core packages above now advertise 3.14 support. If you’ve held off, the blocker list is getting short.

What to actually do this week

  • Pin uv 0.9.7 on one service and record CI time-to-green before/after. Roll out if it pays off.

  • Bump Ruff to 0.14.3 in pre-commit; ship new rules as warnings for one sprint, then enforce.

  • Move base images and build steps to pip 25.3, run a quick editable-install check, then lock.

  • Data teams: evaluate Polars 1.35.1 where you want faster data prep, and keep NumPy 2.3.4 as the 3.14-friendly baseline.

  • If you’re trialing agent workflows, pin pydantic-ai and example versions so behavior is reproducible across environments.

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